Two main approaches exist to predict capacity fade in lithium-ion batteries. On the one hand, (semi-)empirical approaches based on simplified physical equations or data driven methods generate a precise aging quantification from large datasets [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. However, obtaining the necessary datasets is costly and the resulting models can not easily be extrapolated or adapted to new cell chemistries.
On the other hand, physics-based approaches model the underlying aging phenomena and infer equations to describe aging based on operating conditions [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}.
